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Journal articles on the topic 'Empirical methods'

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Published: 4 June 2021
Last updated: 31 January 2023

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1

Lindley, D. V., J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Mathematical Gazette 74, no. 467 (March 1990): 91. http://dx.doi.org/10.2307/3618894.

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2

Bagghi, Parthasarathy, J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Journal of the American Statistical Association 86, no. 413 (March 1991): 244. http://dx.doi.org/10.2307/2289739.

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3

Angus, John E. "Empirical Bayes Methods." Technometrics 33, no. 2 (May 1991): 243–45. http://dx.doi.org/10.1080/00401706.1991.10484821.

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4

Stephenson, W. Robert. "Empirical Bayes Methods." Journal of Quality Technology 22, no. 3 (July 1990): 249–50. http://dx.doi.org/10.1080/00224065.1990.11979250.

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5

Young, Karen, J. Maritz, and T. Lwin. "Empirical Bayes Methods." Applied Statistics 41, no. 3 (1992): 604. http://dx.doi.org/10.2307/2348097.

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6

Denham, Mike, J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Statistician 39, no. 1 (1990): 97. http://dx.doi.org/10.2307/2348214.

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7

Schneider-Mayerson, Matthew, Alexa Weik von Mossner, and W. P. Małecki. "Empirical Ecocriticism: Environmental Texts and Empirical Methods." ISLE: Interdisciplinary Studies in Literature and Environment 27, no. 2 (2020): 327–36. http://dx.doi.org/10.1093/isle/isaa022.

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8

ter Beek, Maurice H., and Alessio Ferrari. "Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods." Software 1, no. 4 (September 24, 2022): 381–416. http://dx.doi.org/10.3390/software1040017.

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Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to external guidelines, and pointers to studies in other fields. We conclude with a number of challenges for empirical formal methods.
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9

Wolstein, Benjamin. "Five Empirical Psychoanalytic Methods." Contemporary Psychoanalysis 26, no. 2 (April 1990): 237–56. http://dx.doi.org/10.1080/00107530.1990.10746657.

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10

Laird, Nan M., and Thomas A. Louis. "Empirical Bayes Ranking Methods." Journal of Educational Statistics 14, no. 1 (March 1989): 29–46. http://dx.doi.org/10.3102/10769986014001029.

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Ranking problems arise in setting priorities for investigations, in providing a simple summary of performance, in comparing objects in a manner robust to measurement scale, and in a wide variety of other applications. Commonly, rankings are computed from measurements that depend on the true attribute. Using the Gaussian model, we propose and compare methods for using these measurements to estimate the ranks of the underlying attributes and show that those based on an empirical Bayes model produce estimates that differ from ranking observed data. These differences result both from the effect of shrinking posterior means towards a common value by an amount that depends on the precision of individual measurements and from the Bayes processing of the posterior distribution to produce estimates that account for the uncertainty in the distribution of the ranks. We illustrate different ranking methods using data on school achievement reported by Aitkin and Longford (1986) . Mathematical and empirical results highlight the importance of using appropriate ranking methods and identify issues requiring further research.
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11

Laird, Nan M., and Thomas A. Louis. "Empirical Bayes Ranking Methods." Journal of Educational Statistics 14, no. 1 (1989): 29. http://dx.doi.org/10.2307/1164724.

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12

Casella, George. "Illustrating empirical Bayes methods." Chemometrics and Intelligent Laboratory Systems 16, no. 2 (October 1992): 107–25. http://dx.doi.org/10.1016/0169-7439(92)80050-e.

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13

Gu, Jiaying, and Roger Koenker. "Empirical Bayesball Remixed: Empirical Bayes Methods for Longitudinal Data." Journal of Applied Econometrics 32, no. 3 (June 20, 2016): 575–99. http://dx.doi.org/10.1002/jae.2530.

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14

Engel, Christoph. "Empirical Methods for the Law." Journal of Institutional and Theoretical Economics 174, no. 1 (2018): 5. http://dx.doi.org/10.1628/093245617x15096094637968.

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15

Gilbert, Daniel, and Martin T. Wells. "Empirical Methods for the Law." Journal of Institutional and Theoretical Economics 174, no. 1 (2018): 24. http://dx.doi.org/10.1628/093245617x15096094637977.

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16

Engel, Christoph, and Urs Schweizer. "Empirical Methods for the Law." Journal of Institutional and Theoretical Economics 174, no. 1 (2018): 1. http://dx.doi.org/10.1628/093245618x15138375893649.

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17

Maritz, J. S., and T. Lwin. "Empirical Bayes Methods, 2nd Edition." Biometrics 46, no. 3 (September 1990): 886. http://dx.doi.org/10.2307/2532124.

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18

Murrell, John N. "Semi-empirical electronic structure methods." Journal of Molecular Structure: THEOCHEM 424, no. 1-2 (February 1998): 93–99. http://dx.doi.org/10.1016/s0166-1280(97)00234-0.

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19

Büscher, Monika, and John Urry. "Mobile Methods and the Empirical." European Journal of Social Theory 12, no. 1 (February 2009): 99–116. http://dx.doi.org/10.1177/1368431008099642.

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20

Backhaus, Klaus, Robert Wilken, Markus Voeth, and Christina Sichtmann. "An Empirical Comparison of Methods." International Journal of Market Research 47, no. 5 (September 2005): 541–60. http://dx.doi.org/10.1177/147078530504700502.

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21

Eisenberg, Theodore. "Empirical Methods and the Law." Journal of the American Statistical Association 95, no. 450 (June 2000): 665–69. http://dx.doi.org/10.1080/01621459.2000.10474247.

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22

Kingdon, Geeta Gandhi. "Empirical Research Methods in Education." Contemporary Education Dialogue 3, no. 2 (January 2006): 171–74. http://dx.doi.org/10.1177/0973184913411116.

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23

Bowen, Harry P. "Empirical methods for international trade." Journal of International Economics 27, no. 1-2 (August 1989): 191–96. http://dx.doi.org/10.1016/0022-1996(89)90087-1.

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24

Persson, Kirsten, and David Shaw. "Empirical Methods in Animal Ethics." Journal of Agricultural and Environmental Ethics 28, no. 5 (July 28, 2015): 853–66. http://dx.doi.org/10.1007/s10806-015-9560-0.

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25

Regenwetter, Michel, Aeri Kim, Arthur Kantor, and Moon-Ho R. Ho. "The Unexpected Empirical Consensus Among Consensus Methods." Psychological Science 18, no. 7 (July 2007): 629–35. http://dx.doi.org/10.1111/j.1467-9280.2007.01950.x.

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In economics and political science, the theoretical literature on social choice routinely highlights worst-case scenarios and emphasizes the nonexistence of a universally best voting method. Behavioral social choice is grounded in psychology and tackles consensus methods descriptively and empirically. We analyzed four elections of the American Psychological Association using a state-of-the-art multimodel, multimethod approach. These elections provide rare access to (likely sincere) preferences of large numbers of decision makers over five choice alternatives. We determined the outcomes according to three classical social choice procedures: Condorcet, Borda, and plurality. Although the literature routinely depicts these procedures as irreconcilable, we found strong statistical support for an unexpected degree of empirical consensus among them in these elections. Our empirical findings stand in contrast to two centuries of pessimistic thought experiments and computer simulations in social choice theory and demonstrate the need for more systematic descriptive and empirical research on social choice than exists to date.
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26

Foster, Michael E., and Karl Sohlberg. "Empirically corrected DFT and semi-empirical methods for non-bonding interactions." Phys. Chem. Chem. Phys. 12, no. 2 (2010): 307–22. http://dx.doi.org/10.1039/b912859j.

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27

Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.

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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
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28

Sampaio, Américo, Alexandre Vasconcelos, and Pedro R. Falcone Sampaio. "Assessing agile methods: an empirical study." Journal of the Brazilian Computer Society 10, no. 2 (November 2004): 22–41. http://dx.doi.org/10.1590/s0104-65002004000300003.

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29

Carpenter, Craig Wesley, Anders Van Sandt, and Scott Loveridge. "Empirical methods in business location research." Regional Studies, Regional Science 8, no. 1 (January 1, 2021): 344–61. http://dx.doi.org/10.1080/21681376.2021.1976261.

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30

Sobrinho, Oswaldo Palma Lopes, Wady Lima Castro Júnior, Leonardo Nazário Silva dos Santos, Gerlange Soares da Silva, Álvaro Itaúna Schalcher Pereira, and Germanna Gouveia Tavares. "Empirical methods for reference evapotranspiration estimation." Scientia Agraria Paranaensis 19, no. 3 (November 3, 2020): 203–10. http://dx.doi.org/10.18188/sap.v19i3.21487.

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The reference evapotranspiration (ETo) is a parameter applied in meteorological, climatological and hydrological studies being of great importance for the planning, management and management of water resources. Due to the need for knowledge about the loss of water from vegetated surfaces, researchers have developed a variety of methods for estimating ETo, some of which are well accepted, while others are criticized, such as the Penman-Monteith-FAO (PM-FAO) meteorological data, which is not always available by limiting its use. The PM-FAO method has been used for decades to be considered internationally and most appropriate for ETo estimates. This method represents the physical phenomenon of evapotranspiration that involves almost all meteorological elements analyzed in automatic and conventional meteorological stations. On the other hand, when this method is impossible to apply due to the lack of some input data, alternative methods are justified (Hargreaves-Samani, Blaney-Criddle, Priestley-Taylor, Jensen-Haise, Camargo, Makkink, Kharrufa , Linacre, Class A tank, Thornthwaite and solar radiation) according to soil and climatic conditions.
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31

Leyland, Alastair H., and Carolyn A. Davies. "Empirical Bayes methods for disease mapping." Statistical Methods in Medical Research 14, no. 1 (February 2005): 17–34. http://dx.doi.org/10.1191/0962280205sm387oa.

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32

Tate, Robyn L., Christine Taylor, and Vanessa Aird. "Applying Empirical Methods in Clinical Practice." Journal of Head Trauma Rehabilitation 28, no. 2 (2013): 77–88. http://dx.doi.org/10.1097/htr.0b013e31824e103e.

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33

Tufis, D. "Empirical Methods for Exploiting Parallel Texts." Literary and Linguistic Computing 17, no. 3 (September 1, 2002): 368–70. http://dx.doi.org/10.1093/llc/17.3.368.

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34

Martinsek, Adam T. "Empirical bayes methods in sequential estimation." Sequential Analysis 6, no. 2 (January 1987): 119–37. http://dx.doi.org/10.1080/07474948708836120.

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35

Reed, David, Doug Baldwin, Michael Clancy, Allen Downey, and Stuart Hansen. "Integrating empirical methods into computer science." ACM SIGCSE Bulletin 34, no. 1 (March 2002): 48–49. http://dx.doi.org/10.1145/563517.563356.

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36

Sampson, Geoffrey. "Quantifying the shift towards empirical methods." International Journal of Corpus Linguistics 10, no. 1 (March 11, 2005): 15–36. http://dx.doi.org/10.1075/ijcl.10.1.02sam.

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In recent decades there has been a trend towards greater use of empirical data, for instance corpus data, within linguistics. I analyse a sample of linguistics articles from the past half-century in order to establish a detailed profile for this trend. Based on consistent criteria for classifying papers as evidence-based, intuition-based, or neutral, the resulting profile shows that the trend (i) is real, but (ii) is strikingly weaker in general linguistics than in the special subfield of computational linguistics, and (iii) appears to have begun to go into reverse.
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37

Nelson, P. G. "Semi-empirical Methods of Quantum Chemistry." Analytica Chimica Acta 186 (1986): 340–41. http://dx.doi.org/10.1016/s0003-2670(00)81812-7.

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38

Haghani, Milad, and Majid Sarvi. "Crowd behaviour and motion: Empirical methods." Transportation Research Part B: Methodological 107 (January 2018): 253–94. http://dx.doi.org/10.1016/j.trb.2017.06.017.

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39

Flynn, Barbara B., Sadao Sakakibara, Roger G. Schroeder, Kimberly A. Bates, and E. James Flynn. "Empirical research methods in operations management." Journal of Operations Management 9, no. 2 (April 1990): 250–84. http://dx.doi.org/10.1016/0272-6963(90)90098-x.

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40

Greis, Noel P., and C. Zachary Gilstein. "Empirical Bayes methods for telecommunications forecasting." International Journal of Forecasting 7, no. 2 (August 1991): 183–97. http://dx.doi.org/10.1016/0169-2070(91)90053-x.

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41

Sampaio, Américo, Alexandre Vasconcelos, and Pedro R. Falcone Sampaio. "Assessing agile methods: An empirical study." Journal of the Brazilian Computer Society 10, no. 2 (June 2004): 21–48. http://dx.doi.org/10.1007/bf03192357.

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42

Lauer, Gerhard. "Introduction: Empirical Methods in Literary Studies." Journal of Literary Theory 9, no. 1 (January 1, 2015): 1–3. http://dx.doi.org/10.1515/jlt-2015-0001.

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43

Sommerfeld, Thomas, and Rebecca J. Weber. "Empirical Correlation Methods for Temporary Anions." Journal of Physical Chemistry A 115, no. 24 (June 23, 2011): 6675–82. http://dx.doi.org/10.1021/jp202817d.

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44

Bell, Stephen. "Semi-empirical methods of quantum chemistry." Endeavour 10, no. 1 (January 1986): 52–53. http://dx.doi.org/10.1016/0160-9327(86)90065-7.

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45

Stenbakken, G. N., and H. Liu. "Empirical Modeling Methods Using Partial Data." IEEE Transactions on Instrumentation and Measurement 53, no. 2 (April 2004): 271–76. http://dx.doi.org/10.1109/tim.2003.822481.

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46

Efron, Bradley. "Empirical Bayes Methods for Combining Likelihoods." Journal of the American Statistical Association 91, no. 434 (June 1996): 538–50. http://dx.doi.org/10.1080/01621459.1996.10476919.

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47

Sridhar, M. R., and M. M. Yovanovich. "Empirical methods to predict Vickers microhardness." Wear 193, no. 1 (April 1996): 91–98. http://dx.doi.org/10.1016/0043-1648(95)06681-0.

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48

Sauder, T. "Fidelity of Cyber-Physical Empirical Methods." Experimental Techniques 44, no. 6 (May 19, 2020): 669–85. http://dx.doi.org/10.1007/s40799-020-00372-x.

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AbstractCyber-physical empirical methods enable to address problems that classical empirical methods alone, or models alone, cannot address in a satisfactory way. In CPEMs, the substructures are interconnected through a control system that includes sensors and actuators, having their own dynamics. The present paper addresses how the fidelity of CPEMs, that is the degree to which they reproduce the behaviour of the real system under study, is affected by the presence of this control system. We describe an analysis method that enables the designer of a CPEM to (1) identify the artefacts (such as biases, noise, or delays) that play a significant role for the fidelity, (2) define bounds for the describing parameter of these artefacts ensuring high-fidelity of the CPEM, and (3) evaluate whether probabilistic robust fidelity is achieved. The proposed method is illustrated by considering a substructured slender structure subjected to dynamic loading.
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49

Orville-Thomas, W. J. "Semi-empirical methods of quantum chemistry." Journal of Molecular Structure: THEOCHEM 136, no. 3-4 (April 1986): 396. http://dx.doi.org/10.1016/0166-1280(86)80155-5.

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50

Bar, Haim, and Kangyan Liu. "Empirical Bayes methods in variable selection." Wiley Interdisciplinary Reviews: Computational Statistics 11, no. 2 (October 17, 2018): e1455. http://dx.doi.org/10.1002/wics.1455.

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